Trevor Campbell
31 papers · 2013–2025 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (32) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
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Deep Specialist
(24)
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Keyword Champion
(5)
π§
Keyword Pioneer
β‘
Prolific Year Β· Productive
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Trend Setter
β
The Questioner
ποΈ
Keyword Collector
(127)
π₯
Unstoppable Β· Silver
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Renaissance Researcher
(5)
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Century Club Β· Bronze
Top co-authors
Research topics
Machine Learning
(30)
Optimization & Theory
(27)
Artificial Intelligence
(18)
Bayesian & Probabilistic Β· Artificial Intelligence
(17)
Bayesian & Probabilistic Β· Machine Learning
(14)
Mathematics & Optimization
(7)
Core Methods
(7)
Deep Learning
(4)
Mathematics
(4)
Models
(4)
Optimization
(3)
Probability
(3)
Computer Vision
(2)
Analysis
(2)
Application Areas
(2)
Keywords
bayesian inference
(12)
variational inference
(9)
posterior approximation
(9)
markov chain monte carlo
(9)
bayesian coreset
(6)
bayesian nonparametrics
(5)
posterior inference
(5)
uncertainty quantification
(3)
effective sample size
(3)
variational flow
(2)
coreset construction
(2)
bayesian nonparametric model
(2)
normalizing flow
(2)
measure-preserving map
(2)
stochastic process
(2)
dirichlet process mixture
(2)
gibbs sampling
(2)
importance sampling
(2)
kl divergence
(2)
sequential monte carlo
(2)
Papers
AutoStep: Locally adaptive involutive MCMC
ICML 2025
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
AISTATS 2025
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG
UAI 2025
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
ICML 2025
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
AISTATS 2024
Coreset Markov chain Monte Carlo
AISTATS 2024
General bounds on the quality of Bayesian coresets
NeurIPS 2024
Mixed variational flows for discrete variables
AISTATS 2024
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
NeurIPS 2023
MixFlows: principled variational inference via mixed flows
ICML 2023
Bayesian inference via sparse Hamiltonian flows
NeurIPS 2022
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
NeurIPS 2022
Parallel Tempering With a Variational Reference
NeurIPS 2022
Finite mixture models do not reliably learn the number of components
ICML 2021
Parallel tempering on optimized paths
ICML 2021
Sequential core-set Monte Carlo
UAI 2021
Bayesian Pseudocoresets
NeurIPS 2020
Slice Sampling for General Completely Random Measures
UAI 2020
Validated Variational Inference via Practical Posterior Error Bounds
AISTATS 2020
Automated Scalable Bayesian Inference via Hilbert Coresets
JMLR 2019
Data-dependent compression of random features for large-scale kernel approximation
AISTATS 2019
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
AISTATS 2019
Sparse Variational Inference: Bayesian Coresets from Scratch
NeurIPS 2019
Universal Boosting Variational Inference
NeurIPS 2019
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
ICML 2018
Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures
CVPR 2017
Coresets for Scalable Bayesian Logistic Regression
NeurIPS 2016
Edge-exchangeable graphs and sparsity
NeurIPS 2016
Small-Variance Nonparametric Clustering on the Hypersphere
CVPR 2015
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
NeurIPS 2015
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
NeurIPS 2013